Durability prediction of glass/epoxy composite using artificial neural network

Amir Hussain Idrisi; Kehkashan Fatima; Abdel Hamid Ismail Mourad

doi:10.1109/ASET53988.2022.9735032

Durability prediction of glass/epoxy composite using artificial neural network

Amir Hussain Idrisi
, Kehkashan Fatima
, Abdel Hamid Ismail Mourad

Department of Mechanical and Aerospace Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

Abstract

This research article examines the prediction capability of the artificial neural network for the durability of FRP composite. In this study the glass/epoxy composite was immersed under harsh environment for the duration of 11 years. The temperature of the seawater was maintained at 23°C, 45°C, and 65°C. The durability of the samples was evaluated in terms of the tensile strength of the conditioned samples. Furthermore, the feedforward backpropagation technique was used in which exposure temperature (°C) and time (months) was used as an input variable and tensile strength was set as an output variable. The results revealed that the established prediction model is promising for the forecasting of the durability of composite.

Original language	English
Title of host publication	2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665418010
DOIs	https://doi.org/10.1109/ASET53988.2022.9735032
Publication status	Published - 2022
Event	2022 Advances in Science and Engineering Technology International Conferences, ASET 2022 - Dubai, United Arab Emirates Duration: Feb 21 2022 → Feb 24 2022

Publication series

Name	2022 Advances in Science and Engineering Technology International Conferences, ASET 2022

Conference

Conference	2022 Advances in Science and Engineering Technology International Conferences, ASET 2022
Country/Territory	United Arab Emirates
City	Dubai
Period	2/21/22 → 2/24/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Artificial neural network
Durability prediction
glass/epoxy composite
long-term immersion

ASJC Scopus subject areas

Process Chemistry and Technology
Artificial Intelligence
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering
Industrial and Manufacturing Engineering
Mechanical Engineering
Waste Management and Disposal

Access to Document

10.1109/ASET53988.2022.9735032

Cite this

Hussain Idrisi, A., Fatima, K., & Hamid Ismail Mourad, A. (2022). Durability prediction of glass/epoxy composite using artificial neural network. In 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022 (2022 Advances in Science and Engineering Technology International Conferences, ASET 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASET53988.2022.9735032

Durability prediction of glass/epoxy composite using artificial neural network. / Hussain Idrisi, Amir; Fatima, Kehkashan; Hamid Ismail Mourad, Abdel.
2022 Advances in Science and Engineering Technology International Conferences, ASET 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 Advances in Science and Engineering Technology International Conferences, ASET 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Hussain Idrisi, A, Fatima, K & Hamid Ismail Mourad, A 2022, Durability prediction of glass/epoxy composite using artificial neural network. in 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022. 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022, Institute of Electrical and Electronics Engineers Inc., 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022, Dubai, United Arab Emirates, 2/21/22. https://doi.org/10.1109/ASET53988.2022.9735032

Hussain Idrisi A, Fatima K, Hamid Ismail Mourad A. Durability prediction of glass/epoxy composite using artificial neural network. In 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (2022 Advances in Science and Engineering Technology International Conferences, ASET 2022). doi: 10.1109/ASET53988.2022.9735032

Hussain Idrisi, Amir ; Fatima, Kehkashan ; Hamid Ismail Mourad, Abdel. / Durability prediction of glass/epoxy composite using artificial neural network. 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 Advances in Science and Engineering Technology International Conferences, ASET 2022).

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title = "Durability prediction of glass/epoxy composite using artificial neural network",

abstract = "This research article examines the prediction capability of the artificial neural network for the durability of FRP composite. In this study the glass/epoxy composite was immersed under harsh environment for the duration of 11 years. The temperature of the seawater was maintained at 23°C, 45°C, and 65°C. The durability of the samples was evaluated in terms of the tensile strength of the conditioned samples. Furthermore, the feedforward backpropagation technique was used in which exposure temperature (°C) and time (months) was used as an input variable and tensile strength was set as an output variable. The results revealed that the established prediction model is promising for the forecasting of the durability of composite.",

keywords = "Artificial neural network, Durability prediction, glass/epoxy composite, long-term immersion",

author = "\{Hussain Idrisi\}, Amir and Kehkashan Fatima and \{Hamid Ismail Mourad\}, Abdel",

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language = "English",

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AU - Hamid Ismail Mourad, Abdel

PY - 2022

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N2 - This research article examines the prediction capability of the artificial neural network for the durability of FRP composite. In this study the glass/epoxy composite was immersed under harsh environment for the duration of 11 years. The temperature of the seawater was maintained at 23°C, 45°C, and 65°C. The durability of the samples was evaluated in terms of the tensile strength of the conditioned samples. Furthermore, the feedforward backpropagation technique was used in which exposure temperature (°C) and time (months) was used as an input variable and tensile strength was set as an output variable. The results revealed that the established prediction model is promising for the forecasting of the durability of composite.

AB - This research article examines the prediction capability of the artificial neural network for the durability of FRP composite. In this study the glass/epoxy composite was immersed under harsh environment for the duration of 11 years. The temperature of the seawater was maintained at 23°C, 45°C, and 65°C. The durability of the samples was evaluated in terms of the tensile strength of the conditioned samples. Furthermore, the feedforward backpropagation technique was used in which exposure temperature (°C) and time (months) was used as an input variable and tensile strength was set as an output variable. The results revealed that the established prediction model is promising for the forecasting of the durability of composite.

KW - Artificial neural network

KW - Durability prediction

KW - glass/epoxy composite

KW - long-term immersion

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ER -

Durability prediction of glass/epoxy composite using artificial neural network

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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